{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "iris = load_iris()\n",
    "#print(iris.DESCR)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#?SVC\n",
    "clf = SVC(kernel='linear')\n",
    "clf.fit(iris.data, iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       "       0, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 2, 1, 1, 1, 1, 1, 1, 1, 1,\n",
       "       1, 1, 1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
       "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.predict(iris.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = iris.data[0:100,[2,3]]\n",
    "y = iris.target[0:100]\n",
    "\n",
    "clf1 = SVC(kernel=\"linear\")\n",
    "clf1.fit(X, y)\n",
    "\n",
    "clf2 = LogisticRegression()\n",
    "clf2.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from itertools import product\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "def plot_estimator(estimator, X, y):\n",
    "    x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "    y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "    xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                         np.arange(y_min, y_max, 0.1))\n",
    "\n",
    "    Z = estimator.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    plt.plot()\n",
    "    plt.contourf(xx, yy, Z, alpha=0.4, cmap = plt.cm.RdYlBu)\n",
    "    plt.scatter(X[:, 0], X[:, 1], c=y,  cmap = plt.cm.brg)\n",
    "    plt.xlabel('Petal.Length')\n",
    "    plt.ylabel('Petal.Width')\n",
    "    plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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C38z/B99cc3nan6WzcjRpqGoVMKyZ4yd1YDjGmBTW561ktyd5b7Cgu573ev6nVUkjHW/1\n+mfqAwKzSxJXWm33VrI5sC6pa8QVaXLSvCFhxD//ostcQhLEo969irkzyYo5DdN51G92sfvlV63K\nXwbxRwJN3rkdCOe3+fsVNLM3VOP9pLwRX8oNBFvLpW5E7cdhOuxvyWSUhvs1uiybafdrZIgedX3Z\nr3o4rkjijwt/OI+T1p/f5u83edM3cOFKvlJQOHHDuQlNheFiDtgxNmkvKG84+S7uBp5GiccT9jJx\ny4m4SV4SbJJZ0jAZx8rDtr06Vw2v93qW+4f/gn/1+ytVnu2tev0NC/9AaV0f8kIFBEL5+MJ+jthy\nAkdtPJMHhv0/zpswlosOn8D0PtHJ6y2+9dwx8kdcMfY47hrxY7Z5tgDwXul/uGb0GVw15hRe6vM4\nESJUu6q4f9hNXDH2OG4edTHr85dz/efTom8ct5pp+M6D+NaqHzC7+1v8cdj/44mB97AxsJprFt1D\nr9r+eCJe3BEPnoiXQ7YeybHrz05caaVw1urLGbljDP5wHoFQPoFQPgOry7j8y1+21V91zsuI1VNt\nyVZP5Y5UVf5sT6rW2+6t4JoxZ1Dl2U6tpxp/OIAn4uOOef9gv+r09wmNEOGzrh+y1beZsqrR9KrZ\nj+9MHJ0wCQ3Qp3ogG/NXR4eNYiufBGH/HQezuMu8xL67B7Ilfz0hCSbsJ3XKuouY0ecfhFz1RCRa\nHW//nQfjVjeLi+dR66nGHfHiVjfnrvwxTw2cRkiChNz1+EIBSup7cfcnz7PLvZMX+z+MO+LljDWX\n0C3UA4BlhQtZVbCYvtWDKasanXYJ2FyW7uopSxomo1l52H33+/1v4K1ezxN2fbVbj6gwtKqc333y\nwl6f977hNzKjzz+St/Zo+JEiLbS11N6oLTqspHv2mGogKkmT256IlxPXncfU5cmbE5rU2nzDQhGZ\nLCKvicgiEVkiIl+KyJJ9C9OY5lmVv303q3RGQsKA6IqhFYWLqHbv2uvzvt+jmX2ZGieB5n6RT/OX\n/JCrPilhACknwkOuIO/3fDm9E5tWac2S20eBm4F3ie4JZUy7C5WWwebFMHsBpYdBeUk5jO7Gws92\n2DBVmppbRtrUVhvpcGf48lRPJLPjy1atmQjfpqoPq+pSVV3V8Gi3yIyJsSp/++bojWfhDfsT2twR\nDwdvOwJ/o0p4rXHq2u82fTDFyqd97esN+/GkWBXlVk/S6ilfOMBxG89u5k3N3mo2aYjIxIYH8IKI\n/FlEpjRqN6bdWZW/vXfOqivZv+pg/OE8fOEAeaECetb246rFd7TqPDs925je9zEeGXwHH5W8wTfX\nXE6/6iFJK5QO33Js9H6K+Ap7kQAnrj0vqe/4LcdRHOye0ObCxQ8X/4a8YCGesBdXxI0n7OXojd/k\nxPXn4gv78cc+R0GoiJ8tvJ/Sut7khQrwhwP4w3mM2nEYZ6yZyvKCz3li4D08ud/vk+pomL3T0rXp\nbSna4meWlGjVPWPaXeOhqjGjolX+VlTutqGqZvgjAW6f/3cWF81jZeEietUO4OBtR0TvhUjTkqL5\n3HTQBYQlTL27lpdCj9O/eiijdhzG5ry1BKUeELzqZdzWo/nZovv5d7+HWVr0GSN2juGk9efzUt/H\n8UR8hF1BFMUb8dG7bj9u/PCPvN3jX3xc8jZ9awZx1qrLWNDtI8KuECpKRMJ41cuWwDp+seBPnLL+\nQuZ3+4DCYBcO2zoFfyTAoR9NZl73d9nsX8+wqnKG7zqIxwb9lhf7P0LQVY+oi+f2e4ALVlzL6esu\nbr+/7E4g7dVTIlKoqrtaanOarZ7KfQnlYcPjbCluO1OUqeMmszlvbUK7J+wDSV7N5A37eXjWexSH\nuu9pq/Rt5NJxUwi66xL6+sMBbp/3JMN3HbSnLSj1XDBxHNWNti7xh/O4YsktHLX5jBZjXl7wOT89\n5FvUuRN34vWG/Tww+3V61HWuzQjT0R7lXuemaPu4Fa83pk0klId1f2Q3/7WzDYFV7PBVJLWH3PXR\n+ysacaubjxtVx5td8hauFMuk6l31vN/jlYS2JcXzU66IqnPX8GbvJvalauTD0lcJuuqT2gXho5I3\n0jqHSa3FpRMicjVwEtBXRGbEHSoBtrZXYMY0p2GoKg8o75cH5LHws41Oh5WTXLhbtbuTILga7ePk\nUne0XkXjviq4G1X0i7429Tu6I+mt9tqzl5Qk7pklDbGYvZbOlcbTROc2tsf+bHj8D9D8HsfGmKzX\nu3YAvWr7IY1+6HvD/pTLeSMSZuzWoxLaDq88OukGPIguBz5y8ykJbfvvHI0v4k/qGwjnc9zGb6cV\n89e3nJyUjCA61Da+4ti0zmFSazFpqOo6VX0bmKyqM+MeH6lq8vWfMSZBpW8j/+r3MM8OeIBV+Yud\nDqdFivJ58Rz+sd99vNLnSXa5d/LTz/9AYagL3rAPV8SNN+znwO3jOXP1pfjCfrxh/57VWdcsuiep\nMFOXYAlXLv7NnpVP3thrvrPyKgZWlyX0dePmxoUPkBcqJBDKxxv24Q8HOGLziUyoSCzV2pT+NUO5\nYMW1eMN+fOEA/nAAX9jPFUtuo1uwR5v9XXVGzV7richrxF0niiRfXqrqcW0fljG5YWaPfzOt7AaU\n6G/gTw2cxknrz+Pi5Tc6HVpKYULcVn45C7r8l7pYZbuHh/yay768mbBE7yqPSARPJFoU6durf8DR\nm87i4+5v4434mVBxXMIEeLwjt5zKgdsn8N/SGYQkxGGVU+hV1z9l35E7D+XhWe8zq3QGVZ4dHLR9\nAoN3j2jVZzl93cVMrDiBj0rewKVuxlccawmjDbQ0QHhr3NfnAF7gScAPfB+Yl+pFxhio8mxnWtkN\n1MetGAoT4uW+f2dCxfGM3Hmog9Gl9kbvf/JZ11l7qvTVeaJ//n7ET6M1NWK/N9Z76lha9Bkv9nuM\nM9dO5ZT1F6V1/m7BUk5otL15U/LDhUzZdGbrP0ScHnV9OXn9Bft0DpOo2eGp+OEoYKKqXqKqb6jq\nS8C3gbM6JEpjstDH3Wem3Kaj3lXLzJ7/diCilr3e+5mEsq4N4hNGg3p3LW/0fraDIjOZojVLbn0i\nMjjueU8g9XWoMR2kZvYCq/JnTAdqTdK4BnhXRJ4VkaeI3rdhd9EZx2R6lb9Dt07aMw8QzxcJMGnz\naQ5E1LJjNn4LfzgvqT1VJT1fOMDRG7/ZQZGZTJF20ogNSR0I/JXoMtxxqvpAWwQhInkikn41GGNi\nMrnKX1GoK1cuvmPP6iJ3xIMvHODE9ee1ej5jdvc3uf2A/+H/9r+ejf41zfZdUjifO0deyZ0jr2Rp\n4WfN9t3l2cFrvZ/h3/0eZk3+Uo7eeCYHbh9PIJyPqAt/KI+8UAFXfnEHBaFiAqFoeyCUz/Cqgzh1\n3YVsCKxiet9HebX3U+z0RG/dChNmdve3eKH/Q3zcbWaTNcZN9ml2GxERGaaqS2Nfp9ycUFU/2Os3\nFykGHiO6f9XTqnpJo+PlwBNAV+DfwFWq2uz/PttGpHPK5Cp/lb6NvN/jZepddYyrnNLqanlXjzmN\nFYWLEtovWn49Z629LKn/XWU/5p1eLya0Tdp0Gtcsviep77yu73Nb+WWx9wkjuDh+wzl8f9nP+aJ4\nLgu6/pfiYHe+tvlkCsPFVLt38X6Pl9nq28SInWM4aPsEnhz4f/xzwIMo0asRRbl8yS95duAf2erb\nQtBVjzfio2ddX34z7x8Uhrqk/dlNx2qTyn0icr+qXhH7+q0UXVRV93rDQhEpBA4HBgPjUySNd4Bf\nAzOAN4F7VLXZUmOWNDqvTE4ce+u5/n/i0SF3pqxs9/f3PqEwUrynaVHRx/z0kLNT9r3jk6cZWfXV\n1U291HHBxHHUeBK3jvOH8/j5gj8xevsRLca2pGg+Pz/4vKSJc1fEhSCEXeE9bZ6Il0mbTuOqJXe2\neF7jjDbZe0pVrxCREbGvj0rx2KcdblV1l6q+ASQN/IpID2Cwqr6sqmGiVxwn7Mv7mdxWHRmZVOWv\nLpzdwyIv9X28yWPT+z+a8PzZ/ZoeLX5uvz8lPP+0W+oBgjpXTdorot7u+QL1UpfUHpFIQsKAaCW9\n93o2U+nPZI105jReEpG1IvKoiFwgIh21PWR/YHXc87VAn1QdReRSEZkjInMqq3Z2SHAmM+VaeViV\n5KWuDRrvLhuRcOqOkDQhH5FI6tNKct+mzxmGFFuDNMXmNXJDOtuIDAEmAK8BRwJvicjnIjJNRNpz\nCYgPEv6XRYCU3xWq+qCqjlXVsSVFxam6mE4iVZW/MaO7ZcTE+N44atOZTVayO2XthQnPT133vSbP\n0/jYgdvGR3/oNxII5TN58zfSiu3rW07Gl6Lyn6gLV6TRhoURN4dXHpPWeU1mS2v1lKquUdXHVXUq\nMBH4I9GhovT2Kd47G4B+cc/7A80vGzGG3CoPe97Kq+lR2y+p4t3pa75P11BpQt8x277OQdsmJvU9\neNtExmz7ekLfvEgBP/7it/jCgWhdDBX84TzGVR7NYZXpjTqP2jGOKZvOwB/OQ9SFO+LFF/Zz0fLr\n6RIsIRDKB6KJqHt9T6YuvamFM5ps0GIRJhEpIrqb7VHA0UAXopPSbwCvq+rmfQ5C5LvA11JMhH8G\n/Ah4N/aeP1fV95o7l02EmwbxxZoqupbz2Bt1WTkxHiHCK33+zlu9XqAgVMQ5q65kRNUhTfb/b/fX\n+Ff/hwE4fe33OHxr07u6bvGv552eL7LbvZOxW49i5M5DkabGw5rwZdGnzCp5nUA4wNe3nELv2v2o\nc9XwXo+XWJP/JQN3j+CILSfi0+Sda03maJPVUwAiEgR2Ao8D96nql20T4p6E9AlQBASALcB1wFBV\nvUtExgCPEl1y+4iqtviriiUNEy+hyl9ttDxsNiYOY9pbukkjnYomY4FjgOOA6SLyAfA60auMTfsS\npKpWAcOaOT6X6A2FxuyV+Lrikw8DRkfLw1pdcWP2TjoT4fNV9W5VPR44iOjS1wOBN0Xk0/YO0Jh9\n1TDHwZaq2MS41Yc2Zm+lVzsREJFeROc1pgCTgTqiVxzGGGM6iXRqhP+RaJLoRnTy+zXgZlVd376h\nGWOMyTTpXGmsAs6LzS8YY4zpxNKZ0/hNUwlDRH7S9iEZY4zJVK2pp5HK8jaJwhhjTFbYp6TR0o6z\nxmSSmpWr0F3bsSp/xuy9ZpOGiARFpD7uEYxrC4pIfUcFasy+aFh2W/jhKxlXrMmYbNLS1uheVfXF\nPbxxbV5V9XVUoMbsq0wvD2tMNkj7Pg0AETmS6CaCezanUdW/t3VQxrSXaHnYxZSWLKC8pBxGd7M7\nxI1phdbc3Pck0Qp7A4CZwBHAPMCShskqodKy6A64h2GJw5hWas1E+OFE62q8DvwUGAPkt0dQxrS3\nXKzyZ0xHaE3S2AUUAPOBbwBu4ID2CMqYjtBQc6OnZxvl/fIYM7qvzW8Y04LWJI2rgCHAw8C5wFLg\n/vYIypiOECotczoEY7JOaybC16vq4tjXEwBEZP+2D8lkqrqgi4+WlOISZdz+FXg96deHNsbkhtYk\njReBPUlCRAR4GRja1kGZzPPW/N5M/f0EIFpF1O2Ch69+jyMO2OJsYMaYDtXi8JSI3CwiXwL7iciS\nhgewEbB6Gp3A5h1+vnfPEVTV+Kiq8bGrxseO3T7O/+2R7NjtdTo8Y0wHSmdO4y7gWGBD7M+Gx4Gq\nekY7xmYyxAsf7EfqqsDKix8N6OhwjDEOSmeX212qupLoSqmuwKGqukpVN7d3cCYzbN/toy7kTmqv\nD7nYsSv7rzQiFeugtpKGPamMMU1rzeqpG4D7gHsBRGSSiDzdLlGZjDL5wE3k+cJJ7V63MunAfSoT\n77iGZbfx92vYsltjmtaapHEO0Qp+uwFUdSZwSDvEZDLMYftXMOXgDeT7Q3va8v1BThu/mvJB29m8\nw88Hi3qwtiL77vVs2I8qPnHUhesscRjThNasnqomenOfAojIUKI3+O0TETkbuAMIA7er6l/jjj1C\ndP6kJtY0RVVX7+t7mtYRgT9f+QEvfjSAp98ZhEuU70xewXFj1nPtXw7l6XcH4/eEqQu5mXzQBv70\no1kpr0wyVai0DDYvhtjWIhceXc5jb9TZtiLGpNCapHE9MAPoIyLPAJOAq/flzUWkCLgbGE80acwT\nkRdVNX64fXOKAAAVHklEQVQd53mq+va+vI/Zdy4XnD5+DaePX7On7Q8vlvHse4OoC7qpC0Z/f3j7\n097c9Nho7rrkY6dC3SuNE8eYUQcwd2HQEocxjaQ9PKWqrwEnAucDjwOjVfWJfXz/44GZqrpOVTcC\nbwJH7+M5TQd58JX9qalP/L2jLujh6XcHEwpLE6/KXPFDVZMDn9vW6cakkM59Gl1F5C4ReRY4W1X/\no6r/UtX1bfD+A4BVcc/XAn3ingeBR0VkoYhc00yMl4rIHBGZU1m1sw3CMumoqk69cioUFupD+1pJ\n2BnxiWOUf70lDmMaSec7+yHAC/wVmCQiP23D9/cB8WscI0SHqQBQ1amqOhA4AZgqIsekOomqPqiq\nY1V1bElRcRuGZ5pz+IgtCMlLVIf0riLfnz1zGo1ZlT9jmpZO0hijqlep6kvA94muomorG4gWdWrQ\nH1jTuJOqrgGmA+Vt+N6dzuot+cxf3o26YMv/7O8t6MEdz4xiydrCPW2RCHy+uguL1nRBFX55/jwK\n88J43dEE4XZFyPeH+O33m57PqKl3M295t4xfadWQOApee8oShzFx0pkIDzZ8oao1IuJvw/efAfxa\nRHoSTWATgcsaDorIMFVdKiIlRK82Lkt9GtOczTv8fO93X2PByq54PIoq3HrhJ5w7eUVS3w1b/Rx+\n9Sl7JrZ/9/woener5sEffcil0yayMzYk1a2wnod+/D6nHr6ap2YOxiURVGHiyM2MGVaZMo6/zhjG\nLU8ejNsVoT7kZszQSv569ft0L8rMUvMNk+Ndls2kfOgkK9ZkDCCaen+IrzqIRIBQXJMn9lwA3dc6\n4SLyXeCm2NNrY38OVdW7ROQlonei1wHTVPW+ls43eshQnXHb7fsSUs45/hfHsGBVN0Lhr64w8nwh\nnv7ZTMaVVST0HXLxGeyu9RJX0RdQRBTVxCuUgDf636I2+NXvHgFfiPMmL+f2736S0HfmZ7246O6v\nJUyce91hDtu/kudvemsfP2H78lQspsvJB1DRtZwFlfnMnbcNv9tlicPklNMmD/1YVce21C+dbURc\nquqLezQ89+5rwoid/xFVHRp7PB973BU7dpKqDlLVsnQShkn25boiFq/tkpAwAGrrXTzwUuLO9hu2\n+lMkDABBNXk1VF3QnZAwouf18MTbQwhHEvvf/5+ypJVWwbCbj5d2z4qhqlRV/myoynRG2bnExaSt\nYmcAjzt5slpxsWFrXkLb6i2FSf2a09Q1ajDkorY+8b/Wxm15Kfv6PBEqdrbliGf7SCgPG6vyZ0xn\nZEkjx5UP3EYwxfJXvzfMlNEbE9rGNjEX0VR6cImmPLZfj90UBBJXTx110Ea8nuQVVZGIUNY/O5ZJ\n12+2bxdj7LsgxxXlh7j+mwsS9o3yuMJ0L6zjkuO/pKbexWOvD+bN+b1wu+G0w1cTTQQNySD659A+\nO8nzfXWOPH+IcWUV5PtDuKThSiZCni/EHRcnr5664pQv6JIfTEgceb4Qv/jOp1m15YgxnV1rthEx\nWeqEseu4+/lRNCSAUMTF0aPXc8Nfx/DCrP329HOJ8rdrZ7JkfTFfrOm6p//ph69i2hWzeeLNITz1\nziBcAucetZzvTFrBTY8dwmNvDsUV63vUwRuYODJ51/yeXep4645X+OP0Ebz5aW96d6vhipMXZ/0u\nucZ0Ni2unso2tnoq2dCLz2RXrYfGK6Kiktv83jB1cRPcef4Qd35vDmcfGX/zPjzy2lBu/vtoauri\n+vpCXHTMUn55/vy2/RAZYM8qqt6TWLAOFn620VZQmZzRZqunTHab+WnPFAmjKdE+dY1WRNXUefj9\niyOTek97cWRCwgCoqffw2BvDklZPGWNygyWNHPfl+i5NHBHSSyRRW3YEktq2VqVe9VQXTF49lVOs\nyp/pxHL4O9sAHH/ouiaOpF75lIoQYezwiqT20UNSr7bqV1Kd1XtPNafx/RpfbK5yOiRjOpQljSxR\nW+9i1eaCtH6Dr613MeuLEjZuDTCgRzUTR24iMUE0/XVhoD5hlRREyA+E+cU5nwHw6GuDefyNQQDc\nfN588v2huE0LlTxfiNu/Oxdp4iImHBHWbMnfsx1JNklV5c/Kw5rOxlZPZThVuOOZch54qQyR6PPL\nT1rMT7+1IOUP5isfGMs/3hmy5/nAnru48+LZfLCoZ0K/UQO2srqigKoaP/GJ4ztHLuHPM8qJnyiv\nDwoP/Gc4T8Wd95qHxvH945Zw/pRlPPTqcCIRRQSOG7OOKQcn3v/R4F+z+vOzhw+lus5DOCIcf+g6\n/u+y2RQEQin7Z6LGxZrKS8qZG65jRSU2KW46BVs9leHun17Gb58rpzpuwjnfH+K6sxZwxSmLE/r+\n7vmR3PHMgaReJUUT7a3pG98ebfO6wgQjiaunLj1xMTd+e0FCbB8tLuXsX09K2ErE5w0zqXwjj1/3\nHtnGU7EYX88IeYeVU9E1Wh7W9qMy2cxWT+WI+6aPSEgYANV1Hu6bPiKp7x9eHEGqfaMS/2xOU31T\ntUe/jk8YEF099ZdX909aPTXt3yOoqU8sKV8fdPPOgt5s3JY8yZ7pGg9VjRnltf2oTKdgSSPDNbVC\nKVV74w0Bm9e61VOtUVvvTpp7WbWlIOX7+TzhJvelynRWHtZ0RpY0Mtz+fVPvy1TWb0dSW+9u1aS7\nIqo1q6fS7xfVq1tN0uqpCSO24HEnr6gKhl0M75u9K5CsPKzpbCxpZLhbLvyk0Wqm6AqlX10wD4Bd\nNR6274quRPqqYl7iiqjkjQVb+jr9vl5XKKHka54vxK0XfJI0Sf+j074g3x+O26cK8v1BrjptUVZN\nhKdi5WFNZ2JJI8NNOnATz9z4Nl8ftZFeXWv4+qjo87L+O/jmbZMYcekZlP/PN5jys+Po3b2GJ657\nh77dq3FJhHx/kB+duog1jz7DsD47abi68Lgi/Pbi2Zw6bmXsXXTP481bXkh4DhDwhHjllldjZV2j\n7X5PmPd+O53//OpNphwcjW38iM387bp3OXlc8r0h/Uuree22GZx5xGp6daumfOA2fjd1Nlef8Xm7\n/x12hIbE0WXZTEscJqfZ6qksFI4IE35yEmsr8glHGvK+Upwf5L/3/Cet8qkrNxVw1A0nJEyyez1h\nQiFBk+Y7lEOGVvLKLW+06efIRVblz2QrWz2Vw2Z+1ovKnf64hAEgBEMunn53UFrn+Murw6kPJY4h\nBUPuFAkjeu5PlpXsS8idhlX5M7nOkkYWWrOlgFCKDQFr6j0s31iU1jm+XF9MKOxuuaNptYQqfyXV\nVuXP5BRLGlnowEHbcKVYLVsQCHJok9X3Eo0v20LAm+4EdG4NYXaE+s0u2JK9q8KMaYoljSx0yNCt\nHDqsIuGHvtcdprS4jtPGr0nrHBcevYyCQAi3K3HlU74/SKpVUt+ZtLwtQjfGZDnHk4aInC0iK0Rk\nqYhc3OhYuYjMF5FVIjJNRByPNxOIwOPXvcsPTv2CPt2rKSmu5dyjVvDKLa+lXTq1pLie126fwRkT\nV9O9qI4Bpbu4/psLWPrQCxwytJL41VPfmbScey+b034fyBiTNRzdsFBEioC7gfFAGJgnIi+q6pZY\nl/uBG4AZwJvAacALTsSaaQK+CNd/cyHXf3PhXp+jX0kNf7jiv0nttkrKGNMUp39zPx6YqarrVHUj\n0cRwNICI9AAGq+rLqhoGngBOcC5UY4wxTieNAUB84em1QJ/Y1/2B1U0cSyAil4rIHBGZU1mVetsN\nY5xjVf5M7nA6afiA+O+mCNFhqpaOJVDVB1V1rKqOLSkqbpdAjWmtmpWrrMqfyTlOJ40NQL+45/2B\nNWkcMyajWZU/k6ucThozgONFpKeI9AYmxtpQ1dXAbhGZLCJu4ALgGedCNaZ1UiWOunCdJQ6T1RxN\nGrHJ758DHwLvA9cAx4nItbEuFwHTgJXAO6qafSXeTKfWOHFceHShbStisprjNcJV9RHgkSaOzQUO\n7Mh4jGlrjeuKX3h0tDzsisrdtpGhyTpOD08Z0ylYeViTKyxpGNNBrDysyQWWNIzpQFYe1mQ7SxrG\ndDArD2uymSUNYxxg5WFNtrKkYYxDQqVlVM/aZFX+TFaxpGGMg1KVhzUmk1nSMMZhDVX+enq2OR2K\nMS2ypGGMMSZtljSMMcakzZKGMcaYtFnSMMYYkzZLGsZkGFt2azKZJQ1jHBYqLaNm5SoiFesS7tcw\nJhNZ0jAmA6Sq8mflYU0msqRhTAaw8rAmW1jSMCZDWHlYkw0saRiTQaw8rMl0ljSMyTDxiaOnZ5tt\nZGgyiiUNYzJQqLRsz9fl/fIYM7qvg9EY8xVLGsYYY9LmWNIQkX4i8p6IrBGRp0Qk0Oj4ZBHZKSJL\nY4+rnYrVGGNMlJNXGr8GnlDVAUAIuDxFn3+q6rDY456ODc8YY0xjTiaNU4BHYl8/BpzgXCjGGGPS\n4UjSEJFuQI2q1sSa1gJ9GnVT4CQRWRYbvurZoUEakwEiFeugttLpMIzZo92Thoj8SUTmxD+Ag4D4\nzXUiQDj+dao6U1V7AiOADcDvmnmPSxvOXVm1sx0+hTEdL/5+DaixZbcmI7R70lDVy1R1bPwDeBfo\nKiK+WLf+wJomXh8EHgLKm3mPBxvOXVJU3NYfwRhHxN+vMcq/3u7XMBnBkeEpVY0AbwPnxpq+CzwT\n30dEBomIR0QEOA/4qCNjNCYTNCSOwg9fSdgB1xKHcYqTE+FXApeLyFqgFnhSRIpE5D8i4gYmAyuB\npcAQ4AanAjXGSQ2Jo8uymZY4jOM8Tr2xqq4AxjdqrgJOjn39CF+trjKmUwuVllE9azGlJQsoLymH\n0d2YO28bKyp3M7ikwOnwTCdid4QbkyVCpWVJW6fbFYfpaJY0jMki1ZGRKROHMR3FkoYxWcaq/Bkn\nWdIwJss0VeXPEofpCJY0jMlCVh7WOMWShjFZysrDGidY0jAmiyWVhz15oK2oMu3KkoYxWa4hcTSw\nKn+mPVnSMMYYkzZLGsYYY9JmScMYY0zaLGkYY4xJmyUNY3KJVfkz7cyShjE5wqr8mY5gScOYHGBV\n/kxHsaRhTI6Ir/JnicO0F0saxuQQKw9r2pslDWNyjJWHNe3JkoYxOShaHnaTFWsybU5U1ekY2pSI\nVAGLnY6jHZUCFU4H0U5y+bOBfb5sl8ufLwCsVdUTWuqYi0ljjqqOdTqO9pLLny+XPxvY58t2ufz5\nWvPZbHjKGGNM2ixpGGOMSVsuJo0HnQ6gneXy58vlzwb2+bJdLn++tD9bzs1pGGOMaT+5eKVhjDGm\nnVjSyCIikici+zsdhzGm88qZpCEiZ4vIChFZKiIXOx1PWxKRYhF5AdgEXO90PG1NRAIi8qCILBGR\nVSJytdMxtSURcYnIa7HPt1hEjnc6prYmIj4R+VxE/uJ0LO1BRFbGfrYsFZF3nY6nLYlIFxF5SkTW\nicgyEfE12z8X5jREpAj4HBgPhIF5wIGqusXRwNqIiBQChwODgfGqeonDIbUpESkBJgP/BEqAhcBY\nVV3jZFxtRUQE6K2qG0TkBODWXFvvLyI3A+OA9bn2/xOiSUNVBzkdR3sQkceAJcBtgB+o02YSQ65c\naRwPzFTVdaq6EXgTONrhmNqMqu5S1TeAkNOxtAdVrVTV5zSqAlgDdHU6rrYS+1wbYk8HAvOdjKet\nichI4DDgaadjMa0jIr2BicDtsf+ntc0lDMidpDEAWBX3fC3Qx6FYzD4QkXKiWxoscDqWtiQi14tI\nJXA18Cun42krsauo3wNXOR1LO6uJDd3MyrHhxVHACuC52NDpXbF/0yblStLwAfG7sUWIDlOZLCIi\npcDfgO+19NtOtlHVO1W1BLgReLWlb8wscjnwtqoudTqQ9qSqI1V1KHAd8ISI5MqVcE/gAOBHwBjg\nCODU5l6QK0ljA9Av7nl/okMcJkuISDdgOnCjqs52Op72oqr/BAqJzt3kgguAc0RkHtErqDNE5DqH\nY2o3qvousBIY5GwkbWYz8LGqrlXV3cBrQFlzL8iVpDEDOF5EesaN0c1wOCaTJhEpBl4kOkH8stPx\ntDURGRL7f4mITABqY3M3WU9VJ6rqgao6Gvhf4HlV/a3TcbUlESkQkT6xrw8hOvT9pbNRtZlZwAEi\n0ldE/MAxwJzmXuDpkLDamapuFJGfAx/Gmq6JZc2cEFsd9glQBAREZDIwVVXfcjSwtnMlcAhwr4jc\nG2s7TlWXOxhTW+oKvCIibqLLpr/tcDymdfKBmbF/vx3A+bny80VVd4vIj4heYfiBR1r6uZITS26N\nMcZ0jFwZnjLGGNMBLGkYY4xJmyUNY4wxabOkYYwxJm2WNIwxxqTNkoYxxpi0WdIwpo2IiKMbSorI\nIyJyvpMxmNxnScN0SiKisdoIq0TkeRHp3kzfIhH55V6+z9si8rW9j7TZc+91XMbsLUsaprMKq+ow\nonsIrQF+0UzfEuC8jgiqlTI1LpPDLGmYTi22m+6rxDagE5FjRWSeiHwpIreISD7wNrBf7MpkpIhM\nEZHPYtXc/iEirfo+EpFBIvJmrJLf87G9jQbFzn+PiKwWkddFJC/Wf7yIfBqrjHd3rF9SXLHTjxaR\nj0Rko4ic2zZ/S8Z8xZKG6dRiP5jPB16PDVHdSrSK4EjgKKI7fk4GVqvqMFVdBGwluoX0EKLJ5shW\nvu1DwNWquj+wGLg01j6YaPXCQYCX6I6xXuBJ4ApVPQDYDaCq1SniAjiQ6Iad55JDdTtM5siJDQuN\n2QtuEfkCqAOeAv4InEw0ScyK9SkEhpK86+dqYCpwMLAfidvyNyu2+eTXgH/ESmr4gRdih9fHtt5G\nRN4jWuVvOLBdVd+L9fkb0YTQlGdUNRSrYz0g3biMSZclDdNZhVV1RHyDiHiAN1T1rEbtgxq99iWi\nieYmolcErSmo5AZ2pXjvQUQTWINgrG8+UB/X7m3h/LUAqhqM7cpqTJuy4SljvjIbmCQiwwBiW9AD\n1ABdRMQdq7hXTjRp7CI6TJU2Vd0ObBCRb8feY4iINHdF8AUwLFbHAb4aykoVlzHtzpKGMTGquo5o\nDe/XRGQZ8MNY+ybgfWAp0eGrO4C5wNPA/MbnEZEzROTauKaXRWR77DEeuBC4QUSWA4+3ENMu4DLg\nBRH5HKggVso4RVzGtDurp2FMFhGRrwO/VNUpTsdiOie70jAmw4nIZIkKANcRnVMxxhGWNIzJfFOB\n9UTnN1YA05wNx3RmNjxljDEmbXalYYwxJm2WNIwxxqTNkoYxxpi0WdIwxhiTNksaxhhj0mZJwxhj\nTNr+P3s1b7oYSAlMAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa861b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZLqI2PNIxOLqjc765kj71AyPBEfM4beWP6BWMv6oes/lwDtg8MaHtgZsnMmbz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\nmYWGMcaYlFloGGOMSdn/B7W95A4stJ22AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa8619b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_estimator(clf1, X, y)\n",
    "plot_estimator(clf2, X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xa7f24e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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ZwqOIRUMqBCISqKL5IM4l3foFUfULRCQ3aLI6CBN1gZmI5B6NELKgr34BtOjq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\nEREA/j/xeTrFIhbdNgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa44cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data = np.array([[-1,2,0],[-2,3,0],[-2,5,0],[-3,-4,0],[-0.1,2,0],[0.2,1,1],[0,1,1],[1,2,1], [1,1,1], [-0.4,0.5,1],[2,5,1]])\n",
    "X = data[:, :2] \n",
    "Y = data[:,2]\n",
    "\n",
    "# Large Margin\n",
    "clf = SVC(C=1.0, kernel='linear')\n",
    "clf.fit(X, Y)\n",
    "plot_estimator(clf,X,Y)\n",
    "\n",
    "# Narrow Margin\n",
    "clf = SVC(C=100000, kernel='linear')\n",
    "clf.fit(X, Y)\n",
    "plot_estimator(clf,X,Y)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=1.0, cache_size=200, class_weight=None, coef0=0.0,\n",
       "  decision_function_shape=None, degree=3, gamma='auto', kernel='linear',\n",
       "  max_iter=-1, probability=False, random_state=None, shrinking=True,\n",
       "  tol=0.001, verbose=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from itertools import product\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.svm import SVC\n",
    "\n",
    "iris = load_iris()\n",
    "X = iris.data[:,[2,3]]\n",
    "y = iris.target\n",
    "\n",
    "clf1 = SVC(kernel=\"rbf\")\n",
    "clf1.fit(X, y)\n",
    "\n",
    "clf2 = SVC(kernel=\"poly\")\n",
    "clf2.fit(X, y)\n",
    "\n",
    "clf3 = SVC(kernel=\"linear\")\n",
    "clf3.fit(X, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "x_min, x_max = X[:, 0].min() - 1, X[:, 0].max() + 1\n",
    "y_min, y_max = X[:, 1].min() - 1, X[:, 1].max() + 1\n",
    "xx, yy = np.meshgrid(np.arange(x_min, x_max, 0.1),\n",
    "                     np.arange(y_min, y_max, 0.1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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A0TAiKWcDkyiqv6W2Ne6VCSZ2lGJPUHXMbjo4rO7Efp1zetN8VKIuQ8PEttJ+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\n/BgIAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xaa704e0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, axarr = plt.subplots(1, 3, sharex='col', sharey='row', figsize=(20, 5))\n",
    "\n",
    "for idx, clf, title in zip([0,1,2],[clf1, clf2, clf3], ['rbf', 'poly', 'linear']):\n",
    "    Z = clf.predict(np.c_[xx.ravel(), yy.ravel()])\n",
    "    Z = Z.reshape(xx.shape)\n",
    "\n",
    "    axarr[idx].contourf(xx, yy, Z, alpha=0.4, cmap = plt.cm.RdYlBu)\n",
    "    axarr[idx].scatter(X[:, 0], X[:, 1], c=y,  cmap = plt.cm.brg)\n",
    "    axarr[idx].set_title(title)\n",
    "\n",
    "plt.show()"
   ]
  },
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   },
   "outputs": [],
   "source": []
  }
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